An Approach Toward More Accurate Forecasts of Air Pollution Levels Through Fog Computing and IoT

Abstract

Rapid urbanization combined with an almost insatiable need for energy has spawned various forms of pollution. Researchers have found air pollution to be at the top of list of factors that cause the most fatalities among urban dwellers today. Scoping urban areas that harbor the most air pollutants and contaminants can help an urban dweller identify comparatively less polluted routes. However, processing of information related to air pollutants is time intensive. As such, temporal forecasting takes preeminence in designing a system that can provide information well in advance of concentration levels of air pollutants at any given time in day or night. In this paper, the authors approach problems related timely forecasts for predicting and tracing air pollution levels across major thoroughfares in urban environments, using fog computing and Internet of Things (IoT). The objective of the research and proposed method is to offer a time-sensitive forecasting to enable citizens to adopt a more agile route-planning approach at any given point of time. In the wake of rising deaths owing to air-borne pollutants and chemicals, results of the research conducted indicate an object-oriented approach toward building a smarter city.